Automated image annotation plays an increasing role in commercial systems. In particular, the medical imaging community relies increasingly on the automated analysis and annotation of large images. Since this automated image analysis may be used to drive patient care decisions, it can be important for the automated results to be validated and appropriately corrected (if necessary) by a knowledgeable user. The user may be a medical doctor, technician, or another individual trained to evaluate medical images and use the software. Outside of a medical context, the user may be anyone who evaluates the image annotations, including either professionals or consumers.
In a commercial context, a particular concern is the ability of a user to provide corrections of automated image annotations, which are repeatable and reproducible from one user to another or from a user to themselves. Particularly in a medical context, diagnosis and treatment decisions may be made on the basis of the (corrected) medical image annotations, so it is important to minimize any dependence of the image annotation corrections on the user. Manual tools for correcting automated image annotations (e.g., picking points, drawing lines, drawing shapes (e.g. polygons or circles), modifying pixel labels, using free text to adjust annotation labels) are subject to substantial levels of inter-user and intra-user variability, which should be controlled in order to provide a reliable image annotation system that maximizes patient benefit.
Thus, a need exists for systems and methods to decrease variability and control user repeatability and reproducibility of automated image annotations.
The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.